Urban residents spend roughly 80% of their time indoors, yet traditional environmental assessments, such as satellite NDVI, measure greenness from a top-down perspective. This fails to capture the human-centric, window-level visual experience of urban greenery, which is crucial for mental well-being and mitigating environmental stressors like extreme heat. By leveraging Google Photorealistic 3D Tiles and semantic segmentation (DeepLabv3+), we can now quantify 3D window-level green visibility, revealing that vertical building position significantly shapes visual exposure to nature.  This project scales this framework into a comparative urban digital twin to address the equity implications of indoor-out green views. During the Summer School 2026, our Team addressed two primary questions: (1) How does 3D window-level green visibility intersect with socioeconomic vulnerabilities across diverse urban morphologies? (2) How does this vertical access to greenery correlate with exposure to localized environmental hazards, specifically extreme urban heat?  We used I-GUIDE's Platform’s HPC to run and parallelize the spatial AI pipeline on a targeted neighborhood sample, leveraged a pre-computed dataset of multiple cities to run geospatial equity models, and synthesized these outputs, translating the statistical spatial models into actionable policy insights for designing climate-resilient cities.